DO STRESS PERCEPTIONS MITIGATE THE LINKS BETWEEN POSITIVE AND NEGATIVE EMOTION VARIABILITY AND INFLAMMATION?
Bibliographic record
Abstract
Emotion variability (the extent to which individuals vary in emotional states over time) has been associated with poorer health indicators (e.g., dysregulated diurnal cortisol) but its associations with inflammation are unknown. In a diverse sample of participants (N=231; aged 25–65; 65% female; 62% Black; 25% Hispanic) we examined if positive emotion variability (PEV) and negative emotion variability (NEV) exhibited linear or curvilinear associations with a composite circulating inflammatory measure and C-reactive protein. Results suggested that PEV and NEV exhibit curvilinear associations with inflammation, with both high and low emotion variability being associated with higher inflammation, but only among older men. These results became non-significant after perception of stress was entered into the model. These data are the first of our knowledge to suggest that PEV and NEV may be associated with inflammation. Our results further suggest that favorable perceptions of stress may mitigate the link between emotional variability and inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".